Electrodes selection for cortical auditory attention decoding with EEG during speech and music listening

Abstract

In complex sound scenes, where multiple sounds are present around a listener, selective attention to one auditory stream is hypothesized to synchronize low-frequency brain activity with the envelope of the attended streams. Recent research has employed stimulus reconstruction from neural data to decode to which auditory stream a listener is paying attention. This could be used to create an auditory attention decoder (AAD), that could be embedded in smart headphones or hearing aids, that would adapt the sound processing based on the attention of the user. However, most of these studies use full scalp electroencephalogram, which is not suitable for implementations in audio devices. To that aim, a smaller EEG device, with fewer electrodes could be used. In the present study, we explore the performance of an AAD based on a smaller number of electrodes during speech and music listening. Participants were presented with two sounds simultaneously, and where asked to attend to one while ignoring the other, and their cortical response was continuously recorded during the lsitening. Using a greedy approach based on reconstruction accuracy, a subset of EEG electrodes that are optimized for linear stimulus reconstruction were selected. The goal of this study is to explore the performance of a linear AAD when reducing the number of electrodes. Results suggest that four well-selected electrodes can be sufficient for a miniaturized AAD as it performs as well as a 64-channels setup. The channels selected vary depending on the type of sound attended, suggesting that different electrodes placement should be used to decode attention during music listening and speech listening.